Abstract: System and methods for training neural network models for real-time flow simulations are provided. Input data is acquired. The input data includes values for a plurality of input parameters associated with a multiphase fluid flow. The multiphase fluid flow is simulated using a complex fluid dynamics (CFD) model, based on the acquired input data. The CFD model represents a three-dimensional (3D) domain for the simulation. An area of interest is selected within the 3D domain represented by the CFD model. A two-dimensional (2D) mesh of the selected area of interest is generated. The 2D mesh represents results of the simulation for the selected area of interest. A neural network is then trained based on the simulation results represented by the generated 2D mesh.

Abstract: Building models and predicting operational outcomes of a drilling operation. At least some of the illustrative embodiments are methods including: gathering sensor data regarding offset wells and context data regarding the offset wells, and placing the sensor data and context data into a data store; creating a reduced data set by identifying a correlation between data in the data store and an operational outcome in a drilling operation; creating a model based on the reduced data set; and predicting the operational outcome based on the model.

Abstract: A system and method for determination of importance of attributes among a plurality of attribute importance models incorporating a segmented attribute kerneling (SAK) method of attribute importance determination. The method permits operation of multiple attribute importance algorithms simultaneously, finds the intersecting subset of important attributes across the multiple techniques, and then outputs a consolidated ranked set. In addition, the method identifies and presents a ranked subset of the attributes excluded from the union.

Abstract: A system and method for determination of importance of attributes among a plurality of attribute importance models incorporating a segmented attribute kerneling (SAK) method of attribute importance determination. The method permits operation of multiple attribute importance algorithms simultaneously, finds the intersecting subset of important attributes across the multiple techniques, and then outputs a consolidated ranked set. In addition, the method identifies and presents a ranked subset of the attributes excluded from the union.

Abstract: Building models and predicting operational outcomes of a drilling operation. At least some of the illustrative embodiments are methods including: gathering sensor data regarding offset wells and context data regarding the offset wells, and placing the sensor data and context data into a data store; creating a reduced data set by identifying a correlation between data in the data store and an operational outcome in a drilling operation; creating a model based on the reduced data set; and predicting the operational outcome based on the model.

Abstract: The present invention provides a dynamic pricing system that generates pricing recommendations for each product in each market. In particular, the system normalizes historic pricing and sales data, and then analyzes this historic data using parameters describing the user's business objectives to produce a pricing list to achieve these objectives. The system uses historical market data to forecast expected sales according to a market segment, product type, and a range of future dates and to determine the effects of price changes on the forecasted future sales. The system further calculates unit costs for the product. The system then estimates profits from sales at different prices by using the sales forecasts, adjusting these sales forecasts for changes in prices, and the costs determinations. The system optionally optimizes prices given current and projected inventory constraints and generates alerts notices according to pre-set conditions.

Abstract: The present invention provides a dynamic pricing system that generates pricing recommendations for each product in each market. In particular, the system normalizes historic pricing and sales data, and then analyzes this historic data using parameters describing the user's business objectives to produce a pricing list to achieve these objectives. The system uses historical market data to forecast expected sales according to a market segment, product type, and a range of future dates and to determine the effects of price changes on the forecasted future sales. The system further calculates unit costs for the product. The system then estimates profits from sales at different prices by using the sales forecasts, adjusting these sales forecasts for changes in prices, and the costs determinations. The system optionally optimizes prices given current and projected inventory constraints and generates alerts notices according to pre-set conditions.